Update app.py

app.py

@@ -13,7 +13,7 @@ from langchain_community.vectorstores import Chroma
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.chains import RetrievalQA
 from langchain import LLMChain, PromptTemplate
-from langchain.agents import AgentExecutor, Tool
+from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
 from langchain.llms import OpenAI
 from PIL import Image
 from decord import VideoReader, cpu
@@ -154,15 +154,27 @@ def handle_input(user_prompt, image=None, video=None, audio=None, doc=None, webs
             )
         )
 
-    # Create an LLMChain using a prompt template and the model
-    prompt_template = PromptTemplate(input_variables=["query"], template="{query}")
-    llm = OpenAI(model=MODEL)
-    llm_chain = LLMChain(llm=llm, prompt=prompt_template)
+    # Add this new code block:
+    prefix = """You are an AI assistant. You have access to the following tools:"""
+    suffix = """Begin!"
 
-    def llm_function(query):
-        return llm_chain.run(query)
+    {chat_history}
+    Human: {input}
+    AI: I will do my best to assist you. Let me think about this step-by-step:"""
 
-    agent_executor = AgentExecutor.from_agent_and_tools(llm=llm_function, tools=tools, verbose=True)
+    prompt = ZeroShotAgent.create_prompt(
+        tools, 
+        prefix=prefix, 
+        suffix=suffix, 
+        input_variables=["input", "chat_history"]
+    )
+
+    llm = Groq(model=MODEL)
+    llm_chain = LLMChain(llm=llm, prompt=prompt)
+
+    agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
+
+    agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True
 
     if image:
         image = Image.open(image).convert('RGB')